Development and Application of a Distributed Hydrological Model Ensemble (DHM-FEWS) for Flash Flood Early Warning
Year: 2026
Type: article
Abstract: Mountain floods, one of the most common and destructive natural disasters worldwide, pose significant challenges to disaster prevention due to their sudden onset, high destructive power, and severe localized impacts. This study proposes an innovative flash flood early warning system based on a distributed hydrological model ensemble. The main objective is to improve the prediction and early warning accuracy of flash flood disasters by integrating multi-source data and regional modeling. The system simulates flood flow and risk levels under different rainfall scenarios to provide timely warnings in mountainous areas. A case study of a heavy rainfall event in Ma Jia Natural Village, Jiangxi Province was used to validate the system’s performance. Through regionalized parameter calibration within the ensemble, the system achieved Nash–Sutcliffe Efficiency (NSE) values exceeding 0.88, while the simulated peak discharges deviated from observed values by only 1.5%, 9.5%, and 4.8% under 3 h, 6 h, and 24 h rainfall scenarios, respectively, demonstrating the improved quantitative accuracy of flood prediction enabled by the ensemble-based framework. The system showed high consistency with observed data, accurately predicting flood responses at 3, 6, and 24 h time scales and providing reliable risk warnings. This approach not only enhances warning accuracy across multiple temporal scales but also supports risk-level early warnings at both river-section and village scales, offering significant practical value for the prevention of mountainous flood disasters.
Source: Water
DOI: 10.3390/w18020237
Authors: Xiao Liu, Kaihua Cao, Ronghua Liu, Yanhong Dou, Min Xie, Delong Li, Hongqing Xu, Yunrui Zhang
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $3,137
Award: G5344849281
OpenAlex ID: W7124433047
